Intelligent management and legal regulation of enterprise green supply chain by fuzzy comprehensive evaluation

基于模糊综合评价的企业绿色供应链智能化管理和法律监管

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Abstract

This study aims to evaluate the risk levels of intelligent management in Green Supply Chain Management (GSCM) through the application of the Fuzzy Comprehensive Evaluation (FCE) model. It begins by outlining the structure of the green supply chain (GSC), encompassing various components such as suppliers, manufacturers, and distributors, while incorporating considerations of environmental and sustainability factors. Data is collected from Company Y through a questionnaire survey, and the FCE model is employed to assess the risk levels of intelligent management within GSCM. The study identifies that Company Y faces high-risk factors in supply chain management, including customer demand fluctuations, inadequate supplier capacity, and reliance on exclusive suppliers, with a risk rating of 4. Additionally, moderate-risk factors, such as partnerships, policies, cash flow, technological capabilities, and internal production management, were rated at a risk level of 3. Based on the risk assessment results, the study proposes several recommendations for improvement. These include reinforcing the enforcement of environmental policies, establishing mechanisms for supply chain information transparency, and promoting clean production technologies. These recommendations are intended to assist the company in better adhering to environmental regulations and fostering sustainable supply chain development. The study's innovation lies in the successful integration of the FCE model into the supply chain risk assessment framework, effectively combining subjective evaluations with objective data to improve the accuracy of the assessment. The findings provide scientific evidence for companies to manage risks and formulate strategies, while also offering new insights into the multidimensional nature of supply chain risks, encompassing social, economic, environmental, and technological dimensions.

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